library(dplyr)
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library(tidyr)
library(tidyverse)
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library(reshape)
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library(ggplot2)
library(GGally)
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library(gridExtra)
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library(plotly)
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import pandas as pd
import numpy as np
from sklearn import preprocessing
import matplotlib.pyplot as plt
import math
import keras
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_squared_error
from sklearn.preprocessing import RobustScaler
from scipy import stats
setwd("~/Desktop/rsdata/")
# Creating a space for all the days data
gen = function(firstday, lastday){
step = seq(firstday,lastday, by = 1)
Rs = "Rs"
rep = rep(Rs, lastday)
for(i in step) {
space =paste0(rep,step)
}
return(space)
}
# First day = 1 Last Day = Last Rs number in File
sample = gen(firstday = 1, lastday = 53)
# reading in
mylist <- list()
for (i in 1:length(sample)){
mylist[[i]] <- read.csv(sample[i])
mylist[[i]] = mylist[[i]][,-c(1,2,4,5)]
colnames(mylist[[i]]) = c("Item", "Price")
}
# Dates
date = seq(as.Date("2020-08-16"), as.Date("2020-10-07"), by="days")
for(i in 1:length(date)){
mylist[[i]]$date <- date[i]
mylist[[i]]$day <- weekdays(as.Date(mylist[[i]]$date))
}
# Merging
data <- merge_recurse(mylist)
# Export
write.csv(data,"RunescapeItems.csv", row.names = FALSE)
# Import
rune = read_csv("RunescapeItems.csv")
## Parsed with column specification:
## cols(
## Item = col_character(),
## Price = col_double(),
## date = col_date(format = ""),
## day = col_character()
## )
## Warning: 5133 parsing failures.
## row col expected actual file
## 3130 Price no trailing characters ,061 'RunescapeItems.csv'
## 3131 Price no trailing characters ,943 'RunescapeItems.csv'
## 3135 Price no trailing characters ,783 'RunescapeItems.csv'
## 3136 Price no trailing characters ,465 'RunescapeItems.csv'
## 3137 Price no trailing characters ,758 'RunescapeItems.csv'
## .... ..... ...................... ...... ....................
## See problems(...) for more details.
check = rune %>% filter(rune$Item == "Abyssal whip")
p = ggplot(check) + aes(date, Price) +
geom_line(linetype = "dashed") +
geom_point(color="deepskyblue", size=1) +
labs(title = "Abyssal whip", x = "Date",y = "Price") +
theme_bw()+
theme(plot.title = element_text(hjust = 0.5))
ggplotly(p)
# Corr
ggpairs(check)
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ab = check[,-c(1,4)]
write.csv(ab,"whip.csv", row.names = FALSE)
df = pd.read_csv(
"whip.csv",
parse_dates=['date'],
index_col="date"
)
df.head()
## Price
## date
## 2020-08-16 69200
## 2020-08-17 68000
## 2020-08-18 69900
## 2020-08-19 69900
## 2020-08-20 71100
# Featuring Eng
df['hour'] = df.index.hour
df['day_of_month'] = df.index.day
df['day_of_week'] = df.index.dayofweek
df['month'] = df.index.month
df.head()
## Price hour day_of_month day_of_week month
## date
## 2020-08-16 69200 0 16 6 8
## 2020-08-17 68000 0 17 0 8
## 2020-08-18 69900 0 18 1 8
## 2020-08-19 69900 0 19 2 8
## 2020-08-20 71100 0 20 3 8
# Training / Testing
train_size = int(len(df) * 0.60)
test_size = len(df) - train_size
train, test = df.iloc[0:train_size], df.iloc[train_size:len(df)]
print(len(train), len(test))
## 31 22
f_columns = ['Price']
f_transformer = RobustScaler()
# tranforming the Prices
f_transformer = f_transformer.fit(train[f_columns].to_numpy())
train.loc[:, f_columns] = f_transformer.transform(
train[f_columns].to_numpy()
)
test.loc[:, f_columns] = f_transformer.transform(
test[f_columns].to_numpy()
)
def create_dataset(X, y, time_steps=1):
Xs, ys = [], []
for i in range(len(X) - time_steps):
v = X.iloc[i:(i + time_steps)].values
Xs.append(v)
ys.append(y.iloc[i + time_steps])
return np.array(Xs), np.array(ys)
time_steps = 7
X_train, y_train = create_dataset(train, train.Price, time_steps)
X_test, y_test = create_dataset(test, test.Price, time_steps)
print(X_train.shape, y_train.shape)
## (24, 7, 5) (24,)
model = keras.Sequential()
# units is the number of neurons
model.add(
keras.layers.Bidirectional(
keras.layers.LSTM(
units=128,
input_shape=(X_train.shape[1], X_train.shape[2])
)
)
)
# Penaltize more Conplex models
model.add(keras.layers.Dropout(rate=0.1))
# Dense is the fully connected layer, Output layer so there is only one
model.add(keras.layers.Dense(units=1, activation='relu'))
# Loss = Error term # Optim for to find the least error
model.compile(loss='mean_squared_error', optimizer='adam', metrics=['acc'])
history = model.fit(
X_train, y_train,
epochs=500,
batch_size=25,
validation_split=0.40,
shuffle=False
)
plt.plot(history.history['loss'], label = 'loss')
plt.plot(history.history['val_loss'], label = 'val_loss')
plt.legend()
plt.show();
# Predicting on the testing data
y_pred = model.predict(X_test)
y_train_inv = f_transformer.inverse_transform(y_train.reshape(-1,1))
y_test_inv = f_transformer.inverse_transform(y_test.reshape(-1,1))
y_pred_inv = f_transformer.inverse_transform(y_pred)
plt.plot(y_test_inv.flatten(), marker = '.', label = 'True')
plt.plot(y_pred_inv.flatten(),'r', marker = '.', label = 'Predicted')
plt.title('Abyssal whip')
plt.legend()
plt.xlim([0, 15])
## (0.0, 15.0)
plt.ylim([70000, 80000])
## (70000.0, 80000.0)
plt.show();
plt.plot(np.arange(0, len(y_train)), y_train_inv.flatten(), 'g', label="history")
plt.plot(np.arange(len(y_train), len(y_train) + len(y_test)),
y_test_inv.flatten(), marker='+', label="true")
plt.plot(np.arange(len(y_train), len(y_train) + len(y_test)),
y_pred_inv.flatten(), 'r', label="prediction")
plt.legend()
plt.title('Abyssal whip')
plt.show();
